10 - Deep Learning 2018

Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises:


	(multilayer) perceptron, backpropagation, fully connected neural networks
	
	
	loss functions and optimization strategies
	
	
	convolutional neural networks (CNNs)
	
	
	activation functions
	
	
	regularization strategies
	
	
	common practices for training and evaluating neural networks
	
	
	visualization of networks and results
	
	
	common architectures, such as LeNet, Alexnet, VGG, GoogleNet
	
	
	recurrent neural networks (RNN, TBPTT, LSTM, GRU)
	
	
	deep reinforcement learning
	
	
	unsupervised learning (autoencoder, RBM, DBM, VAE)
	
	
	generative adversarial networks (GANs)
	
	
	weakly supervised learning
	
	
	applications of deep learning (segmentation, object detection, speech recognition, ...)

Education

Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practices for training and evaluating neural networks visualization of networks and results common architectures, such as LeNet, Alexnet, VGG, GoogleNet recurrent neural networks (RNN, TBPTT, LSTM, GRU) deep reinforcement learning unsupervised learning (autoencoder, RBM, DBM, VAE) generative adversarial networks (GANs) weakly supervised learning applications of deep learning (segmentation, object detection, speech recognition, ...)

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10 - Deep Learning 2018

10 - Deep Learning 2018

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